Course tracking and contour extraction of retinal vessels from color fundus photographs: most efficient use of steerable filters for model-based image analysis

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Abstract
To support ophthalmologists in their daily routine and enable the quantitative assessment of vascular changes in color fundus photographs vessel extraction techniques have been improved. Within a model based approach steerable filters have been tested for efficient and precise segmentation of the vessel tree. The global model comprises the detection of the optic disc, the finding of starting points close to the optic disc, the tracking of the vessel course, the extraction of the vessel contour and the identification of branching points. This helps evaluating image quality and pathological changes to the retina and thus, improves diagnosis and therapy. The optic disc location is estimated and then more precisely extracted with the help of a hierarchical filter scheme based on first- order gaussian kernels at varying orientations. Vessel points are automatically identified around the optic disc and the vessel course is tracked in the actual direction by second-order gaussian kernels at varying orientations and scales. Using this backbone, differently oriented first- order gaussian kernels approximate the vessel contour. Thus, the direction and diameter of each vessel segment are determined. Steerable filters enable most efficient implementation. The developed methods have been applied to color fundus photographs showing different levels of diabetic retinopathy.

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